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山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 9-12.doi: 10.6040/j.issn.1672-3961.2.2014.091

• 机器学习与数据挖掘 • 上一篇    下一篇

一种改进的协方差鉴别学习方法

任捷怡, 吴小俊   

  1. 江南大学物联网工程学院, 江苏 无锡 214122
  • 收稿日期:2015-05-23 修回日期:2014-12-11 出版日期:2015-02-20 发布日期:2015-05-23
  • 通讯作者: 吴小俊(1967-),男,江苏丹阳人,教授,博士,主要研究方向为模式识别.E-mail:xiaojun_wu_jnu@163.com E-mail:xiaojun_wu_jnu@163.com
  • 作者简介:任捷怡(1988-),男,江苏无锡人,博士研究生,主要研究方向为模式识别.E-mail:alvisland@gmail.com
  • 基金资助:
    国家自然科学基金资助项目(61373055)

An improved method of covariance discriminative learning

REN Jieyi, WU Xiaojun   

  1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2015-05-23 Revised:2014-12-11 Online:2015-02-20 Published:2015-05-23

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摘要： 以协方差鉴别学习(covariance discriminative learning, CDL)为基础,对图像集的隶属于黎曼流形之上的协方差矩阵进行双向降维。将降维后的协方差矩阵与有效的黎曼度量,如对数欧氏距离(log euclidean distance, LED)结合得到一个核函数来将这些协方差矩阵映射到欧式空间中进行分类。改进的CDL方法由于减少了协方差矩阵的维数,从而降低了计算复杂度并提高了分类精度。通过在标准数据集上的实验,验证了该改进方法的有效性。

关键词: 人脸识别, 黎曼度量, 协方差矩阵, 鉴别分析, 物体分类

Abstract: Based on the original Covariance Discriminative Learning method, a bidirectional dimension reduction was applied on those covariance matrices, which lied on a Riemannian manifold. Combining these covariance matrices with an efficient Riemannian metric, i.e., log euclidean distance (LED), a kernel function that mapped the covariance matrix from Riemannian manifold to Euclidean space for classification was derived. As a result of dimension reduction of covariance matrices, this method improved accuracy of classification and reduced the complexity of computation. The results were observed through experiments on standard datasets.

Key words: discriminant analysis, riemannian metric, face recognition, covariance matrix, object classification

中图分类号: 

  • TP391.4
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